Information processing apparatus, information processing method, and program

ABSTRACT

Provided are an information processing apparatus, an information processing method, and a program capable of accumulating appropriate relearning data. An information processing apparatus includes an input unit that inputs input data to a learned model acquired in advance through machine learning using learning data, an acquisition unit that acquires output data output from the learned model through the input using the input unit, a reception unit that receives correction performed by a user for the output data acquired by the acquisition unit, and a storage controller that performs control for storing, as relearning data of the learned model, the input data and the output data that reflects the correction received by the reception unit in a storage unit in a case where a value indicating a correction amount acquired by performing the correction for the output data is equal to or greater than a threshold value.

CROSS-REFERENCE TO RELATED APPLICATION

This is a continuation application of U.S. application serial no.16/660,766, filed on Oct. 22, 2019, which claims priority under 35 USC119 from Japanese Patent Application No. 2018-202948 filed on Oct. 29,2018, the disclosure of which is incorporated by reference herein in itsentirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present disclosure relates to an information processing apparatus,an information processing method, and a program.

2. Description of the Related Art

In related art, a technology for generating a calculation expression forfunction approximation through machine learning using learning data isdisclosed (see JP-H07-065168A). In this technology, the calculationexpression for function approximation is changed through relearningusing added learning data.

A technology for identifying a behavior of a user by inputting sensordata acquired from the behavior of the user to a discriminator acquiredin advance through the machine learning using the learning data isdisclosed (see JP2013-041323A).

In this technology, in a case where the user inputs the fact that anidentification result is incorrect, a behavior as a correction candidateis presented to the user, and the user selects the behavior. In thistechnology, the discriminator relearns by using sensor data and thebehavior selected by the user.

SUMMARY OF THE INVENTION

However, in the technologies described in JP1995-065168A(JP-H07-065168A) and JP2013-041323A, since it is not considered how muchcorrection has been performed by the user for output data output from alearned model, appropriate relearning data is not able to beaccumulated. The relearning data mentioned herein indicates data used inthe relearning of the learned model acquired through the machinelearning.

The present disclosure has been made in view of the aforementionedcircumstances, and an object of the present disclosure is to provide aninformation processing apparatus, an information processing method, anda program capable of accumulating appropriate relearning data.

In order to achieve the object, an information processing apparatus ofthe present disclosure comprises an input unit that inputs input data toa learned model acquired in advance through machine learning usinglearning data, an acquisition unit that acquires output data output fromthe learned model through the input using the input unit, a receptionunit that receives correction performed by a user for the output dataacquired by the acquisition unit, and a storage controller that performscontrol for storing, as relearning data of the learned model, the inputdata and the output data that reflects the correction received by thereception unit in a storage unit in a case where a value indicating acorrection amount acquired by performing the correction for the outputdata is equal to or greater than a threshold value.

In the information processing apparatus of the present disclosure, thevalue indicating the correction amount may be a sum of an absolute valueof a ratio of an added portion and an absolute value of a ratio of adeleted portion to and from the output data through the correction.

In the information processing apparatus of the present disclosure, theinput data may be image data indicating a medical image, and the outputdata may be data indicating a region extracted from the image data.

In the information processing apparatus of the present disclosure, thevalue indicating the correction amount may be a ratio of a sum of anarea of an added portion and an area of a deleted portion through thecorrection performed by the user received by the reception unit to anarea of the region indicated by the output data.

In the information processing apparatus of the present disclosure, thevalue indicating the correction amount may be a ratio of a sum of avolume of an added portion and a volume of a deleted portion through thecorrection performed by the user received by the reception unit to avolume of the region indicated by the output data.

In the information processing apparatus of the present disclosure, theoutput data may be a sentence of a medical diagnostic report.

In the information processing apparatus of the present disclosure, thevalue indicating the correction amount may be the number of times thecorrection is performed by the user for the output data.

In the information processing apparatus of the present disclosure, thestorage controller may perform the control in a case where the user is auser determined as a reliable user in advance.

In the information processing apparatus of the present disclosure, thethreshold value may be a value which becomes smaller as a skill level ofthe user becomes higher.

In the information processing apparatus of the present disclosure, thethreshold value may be a value determined depending on a treatment planof a subject.

In the information processing apparatus of the present disclosure, thethreshold value may be 10%.

In the information processing apparatus of the present disclosure mayfurther comprise a learning unit that causes the learned model torelearn by using the relearning data stored in the storage unit by thestorage controller.

In order to achieve the object, an information processing method by acomputer of the present disclosure comprises inputting input data to alearned model acquired in advance through machine learning usinglearning data, acquiring output data output from the learned modelthrough the input, receiving correction performed by a user for theacquired output data, and performing control for storing, as relearningdata of the learned model, the input data and the output data thatreflects the received correction in a storage unit in a case where avalue indicating a correction amount acquired by performing thecorrection for the output data is equal to or greater than a thresholdvalue.

In order to achieve the object, a program of the present disclosurecauses a computer to execute inputting input data to a learned modelacquired in advance through machine learning using learning data,acquiring output data output from the learned model through the input,receiving correction performed by a user for the acquired output data,and performing control for storing, as relearning data of the learnedmodel, the input data and the output data that reflects the receivedcorrection in a storage unit in a case where a value indicating acorrection amount acquired by performing the correction for the outputdata is equal to or greater than a threshold value.

An information processing apparatus of the present disclosure comprisesa memory that stores a command to be executed by a computer, and aprocessor that is configured to execute the stored command. Theprocessor inputs input data to a learned model acquired in advancethrough machine learning using learning data, acquires output dataoutput from the learned model through the input, receives correctionperformed by a user for the acquired output data, and performs controlfor storing, as relearning data of the learned model, the input data andthe output data that reflects the received correction in a storage unitin a case where a value indicating a correction amount acquired byperforming the correction for the output data is equal to or greaterthan a threshold value.

According to the present disclosure, it is possible to accumulateappropriate relearning data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an example of a configuration of adiagnostic support system according to embodiments.

FIG. 2 is a block diagram showing an example of a hardware configurationof an information processing apparatus according to the embodiments.

FIG. 3 is a diagram showing an example of a learned model according to afirst embodiment.

FIG. 4 is a block diagram showing an example of a functionalconfiguration of the information processing apparatus according to thefirst embodiment.

FIG. 5 is a diagram showing an example in which a region is added by auser according to the first embodiment.

FIG. 6 is a diagram showing an example in which a region is deleted bythe user according to the first embodiment.

FIG. 7 is a diagram showing an example in which the region is added anddeleted by the user according to the first embodiment.

FIG. 8 is a flowchart showing an example of a storage control processaccording to the first embodiment.

FIG. 9 is a flowchart showing an example of a relearning processaccording to the first embodiment.

FIG. 10 is a diagram showing an example of a learned model according toa second embodiment.

FIG. 11 is a block diagram showing an example of a functionalconfiguration of an information processing apparatus according to thesecond embodiment.

FIG. 12 is a flowchart showing an example of a storage control processaccording to the second embodiment.

FIG. 13 is a flowchart showing an example of a relearning processaccording to the second embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, form examples for implementing a technology of the presentdisclosure will be described in detail.

First Embodiment

Initially, a configuration of a diagnostic support system 10 accordingto the present embodiment will be described with reference to FIG. 1 .As shown in FIG. 1 , the diagnostic support system 10 includes an imagemanagement apparatus 12 and an information processing apparatus 14. Theimage management apparatus 12 and the information processing apparatus14 are connected to a network N, and can communicate via the network N.The image management apparatus 12 stores image data (hereinafter,referred to as “medical image data”) indicating a medical image acquiredthrough imaging using an imaging device that images medical images ofcomputed tomography (CT) and magnetic resonance imaging (MRI). Examplesof the image management apparatus 12 include a picture archiving andcommunication system (PACS). The information processing apparatus 14supports diagnosis by using the medical image data stored in the imagemanagement apparatus 12. Examples of the information processingapparatus 14 include a personal computer and a server computer.

Next, a hardware configuration of the information processing apparatus14 according to the present embodiment will be described with referenceto FIG. 2 . As shown in FIG. 2 , the information processing apparatus 14includes a central processing unit (CPU) 20, a memory 21 as a temporarystorage region, and a nonvolatile storage unit 22. The informationprocessing apparatus 14 includes a display unit 23 such as a liquidcrystal display, an input unit 24 such as a keyboard and a mouse, and anetwork interface (I/F) 25 connected to the network N. The CPU 20, thememory 21, the storage unit 22, the display unit 23, the input unit 24,and the network I/F 25 are connected to a bus 26.

The storage unit 22 is implemented by a hard disk drive (HDD), asolid-state drive (SSD), and a flash memory. An information processingprogram 30 is stored in the storage unit 22 as a storage medium. The CPU20 reads out the information processing program 30 from the storage unit22, develops the readout information processing program into the memory21, and executes the developed information processing program 30.

A learned model 32 is stored in the storage unit 22. The learned model32 will be described with reference to FIG. 3 . As shown in FIG. 3 , aform example in which a neural network including an input layer, aplurality of interlayers, and an output layer is applied as an exampleof the learned model 32 will be described in the present embodiment.

The medical image data acquired through the imaging using CT is input,as an example of the input data, to the learned model 32. Dataindicating a region extracted from the medical image data is output, asan example of output data, from the learned model 32. In the presentembodiment, the learned model 32 extracts lung regions on a medicalimage indicated by the input medical image data, and outputs image dataindicating an image acquired by filling the extracted lung regions witha predetermined color (for example, red). In FIG. 3 , the extracted lungregions are represented by diagonal regions.

Although a case where the learned model 32 extracts the lung regions onboth left and right sides has been described in the present embodiment,the present disclosure is not limited thereto. The learned model 32 mayextract the lung region on any one of the left and right sides, mayextract regions other than the lung regions, or may extract multiplekinds of regions. Examples of the regions extracted by the learned model32 include an organ region, a bone region, a blood vessel region, and asubcutaneous fat region.

The learned model 32 is a model acquired in advance by machine learningusing, as learning data (referred to as training data), multiple sets ofmedical image data and data indicating the lung regions of the medicalimage data. Examples of a method used in the machine learning in thiscase include an error back-propagation method.

Next, a functional configuration of the information processing apparatus14 according to the present embodiment will be described with referenceto FIG. 4 . As shown in FIG. 4 , the information processing apparatus 14includes an input unit 40, an acquisition unit 42, an output unit 44, areception unit 46, a deriving unit 48, a storage controller 50, and alearning unit 52. The CPU 20 executes the information processing program30, and thus, the information processing program functions as the inputunit 40, the acquisition unit 42, the output unit 44, the reception unit46, the deriving unit 48, the storage controller 50, and the learningunit 52.

The input unit 40 acquires the medical image data from the imagemanagement apparatus 12, and inputs the acquired medical image data tothe learned model 32. The acquisition unit 42 acquires the output dataoutput from the learned model 32 so as to correspond to the input fromthe input unit 40.

The output unit 44 outputs the output data acquired by the acquisitionunit 42 to the display unit 23. Through this output, the image acquiredby filling the lung regions with the predetermined color is displayed onthe display unit 23. A user confirms the image displayed on the displayunit 23, and corrects the lung regions on the image to correct regionsthrough the input unit 24 in a case where correction is necessary.

For example, as shown in FIG. 5 , in a case where the extracted lungregions are narrower than the actual regions, the user performscorrection for adding a region. In the example of FIG. 5 , diagonalregions A indicate the lung regions extracted by the learned model 32,and a white region B indicates a region added by the user through thecorrection.

For example, as shown in FIG. 6 , in a case where the extracted lungregions are wider than the actual regions, the user performs correctionfor deleting a region. In the example of FIG. 6 , the diagonal regions Aindicate the lung regions extracted by the learned model 32, and a whiteregion C indicates a region deleted by the user through the correction.

For example, as shown in FIG. 7 , the user may perform both thecorrection for adding the region B and deleting the region C to and fromthe extracted lung regions.

The reception unit 46 receives the correction performed by the user forthe output data output by the output unit 44. Specifically, thereception unit 46 receives the output data that reflects the correctionof the region performed by the user as stated above.

The deriving unit 48 derives a value indicating a correction amountthrough the correction received by the reception unit 46 for the outputdata output by the output unit 44. In the present embodiment, thederiving unit 48 derives the sum of an absolute value of a ratio of anadded portion and an absolute value of a ratio of a deleted portion toand from the output data through the correction. For example, as in theexample shown in FIG. 5 , in a case where there is the added region andthere is no deleted region, since the absolute value of the ratio of thedeleted portion to the output data is zero, the sum is the absolutevalue of the ratio of the added portion. For example, as in the exampleshown in FIG. 6 , in a case where there is the deleted region and thereis not added region, since the absolute value of the ratio of the addedportion to the output data is zero, the sum is the absolute value of theratio of the deleted portion.

Specifically, the deriving unit 48 derives, as the value indicating thecorrection amount, a ratio of the sum of an area of the added portion(the region B in the examples of FIGS. 5 and 7 ) and an area of thedeleted portion (the region C in the examples of FIGS. 6 and 7 ) throughthe correction received by the reception unit 46 to an area of the lungregions (regions A in the examples of FIGS. 5 to 7 ) extracted by thelearned model 32. For example, this ratio can be obtained by dividingthe sum of the area of the region B and the area of the region C by thearea of the region A. For example, the area of each region may be thenumber of pixels of the image, or may be an area acquired by convertingthe number of pixels into an actual size.

The deriving unit 48 may derive the value indicating the correctionamount by using a volume instead of the area. In this case, a form inwhich pixels of a portion of a plurality of medical images indicated bya plurality of medical image data items acquired by performing CTimaging once which is corrected by the user are used as voxels and thenumber of voxels is used as a volume is illustrated.

In case where the value indicating the correction amount derived by thederiving unit 48 is equal to or greater than a threshold value, thestorage controller 50 performs control for storing, as relearning dataof the learned model 32, the input data input to the learned model 32 bythe input unit 40 and the output data that reflects the correctionreceived by the reception unit 46 in the storage unit 22.

In the present embodiment, a value determined depending on a treatmentplan for a disease of a subject is used as the threshold value. As aspecific example, the following prescription conditions (1) and (2) areset for medicine of polycystic kidney disease depending on the volume ofthe kidney.

-   (1) An increase rate in volume of the kidney is 5%/year or more.-   (2) A total kidney volume is 750 ml or more.

As stated above, in a case where an error of 5% or more occurs at thetime of measuring the volume of the kidney, the treatment plan of thedisease of the subject is influenced. In this example, 5% is used as thethreshold value, and the storage controller 50 performs control forstoring the relearning data in the storage unit 22 in a case where thevalue indicating the correction amount derived by the deriving unit 48is equal to or greater than 5%.

A value corresponding to extraction accuracy of a required region may beused as the threshold value. For example, in a case where DICE accuracyindicating the region extraction in machine learning exceeds 90%, it isdifficult to recognize a difference in accuracy at a glance. Thus, theinfluence may be less for correction of less than 10%, and 10% may beused as the threshold value. In this case, the storage controller 50performs control for storing the relearning data in the storage unit 22in a case where the value indicating the correction amount derived bythe deriving unit 48 is equal to or greater than 10%. A value set by theuser may be used as the threshold value.

The learning unit 52 causes the learned model 32 to relearn by using therelearning data stored in the storage unit 22 under the control of thestorage controller 50. Examples of a method used in the relearninginclude an error back-propagation method.

Next, the actions of the information processing apparatus 14 accordingto the present embodiment will be described with reference to FIGS. 8and 9 . The CPU 20 executes the information processing program 30, andthus, a storage control process shown in FIG. 8 and a relearning processshown in FIG. 9 are performed. For example, the storage control processshown in FIG. 8 is performed in a case where an execution command of thestorage control process is input by the user through the input unit 24.

In step S10 of FIG. 8 , the input unit 40 acquires the medical imagedata from the image management apparatus 12, and inputs the acquiredmedical image data to the learned model 32. In step S12, the acquisitionunit 42 acquires the output data output from the learned model 32 so asto correspond to the input through the process of step S10.

In step S14, the output unit 44 outputs the output data acquired throughthe process of step S12 to the display unit 23 as stated above. In stepS16, the reception unit 46 determines whether or not the correctionperformed by the user for the output data output through the process ofstep S14 is received as stated above. In a case where this determinationis positive determination, the process proceeds to step S18.

In step S18, the deriving unit 48 derives the value indicating thecorrection amount through the correction received through the process ofstep S16 for the output data output through the process of step S14 asstated above. In step S20, the storage controller 50 determines whetheror not the value indicating the correction amount derived through theprocess of step S18 is equal to or greater than the threshold value asstated above. In a case where this determination is positivedetermination, the process proceeds to step S22.

In step S22, the storage controller 50 performs control for storing, asthe relearning data of the learned model 32, the input data input to thelearned model 32 through the process of step S10 and the output datathat reflects the correction received through the process of step S16 inthe storage unit 22. In a case where the process of step S22 is ended,the storage control process is ended.

Meanwhile, in a case where the determination of step S16 is negativedetermination, the processes of steps S18, S20, and S22 are notperformed, and the storage control process is ended. In a case where thedetermination of step S20 is negative determination, the process of stepS22 is not performed, and the storage control process is ended.

Through the storage control process shown in FIG. 8 , in a case where apredetermined number (for example, 100) of relearning data items arestored in the storage unit 22, the relearning process shown in FIG. 9 isperformed. The relearning process shown in FIG. 9 may be performedwhenever the relearning data is stored in the storage unit 22 throughthe process of step S22 of FIG. 8 , or may be performed in a case wherethe execution command is input by the user.

In step S30 of FIG. 9 , the learning unit 52 acquires the relearningdata stored in the storage unit 22 through the storage control processshown in FIG. 8 . In step S32, the learning unit 52 causes the learnedmodel 32 to relearn by using the relearning data acquired through theprocess of step S30. In a case where the process of step S32 is ended,the relearning process is ended.

As described above, according to the present embodiment, in a case wherethe value indicating the correction amount through the correctionperformed by the user for the output data is equal to or greater thanthe threshold value, the control for storing, as the relearning data ofthe learned model 32, the input data and the output data that reflectsthe correction in the storage unit 22 is performed. Accordingly, it ispossible to accumulate appropriate relearning data.

Second Embodiment

The form example in which the medical image data is a processing targethas been described in the first embodiment. A form example in which themedical diagnostic report is a processing target will be described inthe present embodiment. A configuration (see FIG. 1 ) of the diagnosticsupport system 10 and a hardware configuration (see FIG. 2 ) of theinformation processing apparatus 14 according to the present embodimentare the same as those in the first embodiment, and thus, the descriptionthereof will be omitted.

The learned model 32 stored in the storage unit 22 according to thepresent embodiment will be described with reference to FIG. 10 . Asshown in FIG. 10 , a form example in which a neural network including aninput layer, a plurality of interlayers, and an output layer is appliedas an example of the learned model 32 will be described in the presentembodiment.

Lesion information acquired as a result of a diagnostic support processis input as an example of the input data to the learned model 32.Examples of the diagnostic support process include lung computer-aideddiagnosis (CAD), and examples of the lesion information includequalitative information such as a size of a lesion and a signal value ofthe CT image. A fixed phrase used as a sentence of a medical diagnosticreport is output as the example of the output data from the learnedmodel 32.

The learned model 32 is a model acquired in advance through the machinelearning using multiple sets of lesion information items and thesentence of the medical diagnostic report as the learning data. Examplesof a method used in the machine learning in this case include an errorback-propagation method.

Next, a functional configuration of the information processing apparatus14 according to the present embodiment will be described with referenceto FIG. 11 . As shown in FIG. 11 , the information processing apparatus14 includes an input unit 60, an acquisition unit 62, an output unit 64,a reception unit 66, a deriving unit 68, a storage controller 70, and alearning unit 72. The CPU 20 executes the information processing program30, and the information processing program functions as the input unit60, the acquisition unit 62, the output unit 64, the reception unit 66,the deriving unit 68, the storage controller 70, and the learning unit72.

The input unit 60 acquires the lesion information acquired as the resultof the diagnostic support process from the image management apparatus12, and inputs the acquired lesion information to the learned model 32.The acquisition unit 62 acquires the output data output from the learnedmodel 32 so as to correspond to the input through the input unit 60.

The output unit 64 outputs the output data acquired by the acquisitionunit 62 to the display unit 23. Through this output, a fixed phrase usedas the sentence of the medical diagnostic report is displayed on thedisplay unit 23. The user confirms a word (for example, the size of thelesion) and a sentence style indicating features of the fixed phrasedisplayed on the display unit 23, and corrects the fixed phrase to acorrect sentence through the input unit 24 in a case where correction isnecessary.

The reception unit 66 receives correction performed by the user for theoutput data output by the output unit 64. Specifically, the receptionunit 66 receives the sentence that reflects the correction performed bythe user as stated above.

The deriving unit 68 derives the value indicating the correction amountthrough the correction received by the reception unit 66 for the outputdata output by the output unit 64. In the present embodiment, thederiving unit 68 derives the sum of the absolute value of the ratio ofthe added portion and the absolute value of the ratio of the deletedportion to and from the output data through the correction. For example,in a case where there is an added character and there is no deletedcharacter, since the absolute value of the ratio of the deleted portionto the output data is zero, the sum is the absolute value of the ratioof the added portion. For example, in a case where there is the deletedcharacter and there is no added character, since the absolute value ofthe ratio of the added portion to the output data is zero, the sum isthe absolute value of the ratio of the deleted portion.

Specifically, the deriving unit 68 derives, as the value indicating thecorrection amount, a ratio of the sum of the number of added charactersand the number of deleted characters through the correction received bythe reception unit 66 to the number of characters of the fixed phraseoutput from the learned model 32. The deriving unit 68 may derive, asthe value indicating the correction amount, a ratio of the sum of thenumber of added words and the number of deleted words through thecorrection received by the reception unit 66 to the number of words ofthe fixed phrase output from the learned model 32.

In a case where the value indicating the correction amount derived bythe deriving unit 68 is equal to or greater than the threshold value,the storage controller 70 performs control for storing, as therelearning data of the learned model 32, the input data input to thelearned model 32 by the input unit 60 and the output data that reflectsthe correction received by the reception unit 66 in the storage unit 22.

The learning unit 72 causes the learned model 32 to relearn by using therelearning data stored in the storage unit 22 under the control of thestorage controller 70. Examples of a method used in the relearninginclude an error back-propagation method.

Next, the actions of the information processing apparatus 14 accordingto the present embodiment will be described with reference to FIGS. 12and 13 . The CPU 20 executes the information processing program 30, andthus, a storage control process shown in FIG. 12 and a relearningprocess shown in FIG. 13 are performed. For example, a diagnosticsupport process shown in FIG. 12 is performed in a case where anexecution command of the storage control process is input by the userthrough the input unit 24.

In step S40 of FIG. 12 , the input unit 40 acquires the lesioninformation acquired as the result of the diagnostic support processfrom the image management apparatus 12, and inputs the acquired lesioninformation to the learned model 32. In step S42, the acquisition unit62 acquires the output data output from the learned model 32 so as tocorrespond to the input through the process of step S40.

In step S44, the output unit 64 outputs the output data acquired throughthe process of step S42 to the display unit 23 as stated above. In stepS46, the reception unit 66 determines whether or not the correctionperformed by the user for the output data output through the process ofstep S44 is received as stated above. In a case where this determinationis positive determination, the process proceeds to step S48.

In step S48, the deriving unit 68 derives the value indicating thecorrection amount through the correction received through the process ofstep S46 for the output data output through the process of step S44. Instep S50, the storage controller 70 determines whether or not the valueindicating the correction amount derived through the process of step S48is equal to or greater than the threshold value as stated above. In acase where this determination is positive determination, the processproceeds to step S52.

In step S52, the storage controller 70 performs control for storing, asthe relearning data of the learned model 32, the input data input to thelearned model 32 through the process of step S40 and the output datathat reflects the correction received through the process of step S46 inthe storage unit 22. In a case where the process of step S52 is ended,the storage control process is ended.

Meanwhile, in a case where the determination of step S46 is negativedetermination, the processes of steps S48, S50, and S52 are notperformed, and the storage control process is ended. In a case where thedetermination of step S50 is negative determination, the process of stepS52 is not performed, and the storage control process is ended.

Through the storage control process shown in FIG. 12 , in a case where apredetermined number (for example, 100) of relearning data items arestored in the storage unit 22, the relearning process shown in FIG. 13is performed. The relearning process shown in FIG. 13 may be performedwhenever the relearning data is stored in the storage unit 22 throughthe process of step S52 of FIG. 12 , and may be performed when theexecution command is input by the user.

In step S60 of FIG. 13 , the learning unit 72 acquires the relearningdata stored in the storage unit 22 through the storage control processshown in FIG. 12 . In step S62, the learning unit 72 causes the learnedmodel 32 to relearn by using the relearning data acquired through theprocess of step S60 as stated above. In a case where the process of stepS62 is ended, the relearning process is ended.

As described above, according to the present embodiment, it is possibleto acquire the same effects as those in the first embodiment.

Although a case where the disclosed technology is applied to the medicalfield has been described in the embodiments, the present disclosure isnot limited thereto. A form in which the disclosed technology is appliedto fields other than the medical field may be used.

The value indicating the correction amount in the embodiment may be thenumber of times the correction is performed for the output data outputfrom the output unit 44 or 64 by the user. In this case, for example, ina case where N (N is an integer of 2 or more) output data items arecontinuously corrected, a form in which the relearning data is stored inthe storage unit 22 is illustrated. In this case, in a case where aratio of the number of times the correction is performed to apredetermined number (for example, 10) of output data items is equal toor greater than a threshold value (for example, 80%), a form in whichthe relearning data is stored in the storage unit 22 is illustrated.

In the embodiment, in a case where a user who corrects the output datais a user determined as a reliable user, control for storing therelearning data in the storage unit 22 may be performed. In this case, aform in which a doctor, a radiologist, and an engineer among a doctor, aradiologist, an engineer, a resident, and a guest are registered asreliable users is illustrated.

A value which becomes smaller as a skill level of the user becomeshigher may be used as the threshold value compared with the valueindicating the correction amount in the embodiment. In this case, forexample, a threshold value in a case where the user is a medicalspecialist as an expert is a value smaller than a threshold value in acase where the user is the resident as an inexperienced doctor. As theskill level of the user becomes higher, the accuracy of the correctionis high in many cases. Accordingly, it is possible to accumulateappropriate relearning data by using the value which becomes smaller asthe skill level of the user becomes higher as the threshold value.

In the embodiment, for example, various processors to be described belowcan be used as hardware structures of the processing units that performvarious processes such as the input unit, the acquisition unit, theoutput unit, the reception unit, the deriving unit, the storagecontroller, and the learning unit. As stated above, examples of variousprocessors include a programmable logic device (PLD) such as afield-programmable gate array (FPGA) which is a processor of which acircuit configuration can be changed after being manufactured, adedicated electric circuit such as an application specific integratedcircuit (ASIC) which is a processor having a circuit configurationdesigned as a dedicated circuit in order to perform a specific processin addition to the CPU which is a general-purpose processor functioningas various processing units by executing software (program).

One processing unit may be constituted by one of these variousprocessors, or may be constituted by a combination (for example, acombination of a plurality of FPGAs or a combination of the CPU and theFPGA) of the same kind or different kinds of two or more processors.Alternatively, the plurality of processing units may be constituted byone processor.

Firstly, as the example in which the plurality of processing units isconstituted by one processor, there is a form in which one processor isconstituted by a combination of one or more CPUs and software and thisprocessor functions as the plurality of processing units as representedby computers such as a client and a server. Secondly, there is a form inwhich a processor that implements the entire system function includingthe plurality of processing units by one integrated circuit (IC) chip asrepresented by a system on chip (SoC) is used. As stated above, variousprocessing units are constituted as hardware structure by using one ormore of various processors.

More specifically, an electric circuitry acquired by combining circuitelements such as semiconductor elements can be used as the hardwarestructure of these various processors.

Although the aspect in which the information processing program 30 isstored (installed) in advance in the storage unit 22 has been describedin the embodiment, the present disclosure is not limited thereto. Theinformation processing program 30 may be provided while being recordedin a recording medium such as a compact disc read only memory (CD-ROM),a digital versatile disc read only memory (DVD-ROM), and a universalserial bus (USB) memory. The information processing program 30 may bedownloaded from an external device via a network.

EXPLANATION OF REFERENCES

-   10: diagnostic support system-   12: image management apparatus-   14: information processing apparatus-   20: CPU-   21: memory-   22: storage unit-   23: display unit-   24: input unit-   25: network I/F-   26: bus-   30: information processing program-   32: learned model-   40, 60: input unit-   42, 62: acquisition unit-   44, 64: output unit-   46, 66: reception unit-   48, 68: deriving unit-   50, 70: storage controller-   52, 72: learning unit-   N: network

What is claimed is:
 1. An information processing apparatus comprising: a memory; and a processor, coupled to the memory and configured to: input data into a learned model, wherein the learned model is a machine learning model trained in advance on learning data, wherein the data is a plurality of sets of lesion information and at least one sentence included in a medical diagnostic report based on the lesion information; acquire output data from the learned model through inputting the data including lesion information of a subject to be diagnosed, wherein the output data includes at least one sentence of a medical diagnostic report based on the lesion information of the subject; display the at least one sentence included in the output data on a display; receive correction data for the acquired output data via an input device to generate a corrected output data including an added portion that is added to the at least one sentence included in the output data and a deleted portion that is deleted from the at least one sentence included in the output data; store, as relearning data of the learned model, a set of the input data including the lesion information of the subject and the corrected output data in a storage in a case where a value indicating a correction amount of the added portion and the deleted portion included in the corrected output data is equal to or greater than a threshold value; and retrain the learned model by using the stored relearning data in the storage.
 2. The information processing apparatus according to claim 1, wherein the at least one sentence included in the output data indicates features of a fixed phrase of the medical diagnostic report.
 3. The information processing apparatus according to claim 1, wherein the lesion information is acquired as a result of a diagnostic support process for a medical image of the subject.
 4. The information processing apparatus according to claim 1, wherein the value indicating the correction amount is a sum of an absolute value of a ratio of the added portion to the at least one sentence included in the output data and an absolute value of a ratio of the deleted portion to the at least one sentence included in the output data.
 5. The information processing apparatus according to claim 1, wherein the value indicating the correction amount is the number of times the correction is performed by the user for the output data.
 6. The information processing apparatus according to claim 1, wherein the threshold value is selected based on a skill level of the user prior to the generation of the relearning data.
 7. The information processing apparatus according to claim 1, wherein the threshold value is a value determined depending on a treatment plan of the subject.
 8. An information processing method, executed by a computer, comprising: inputting data into a learned model, wherein the learned model is a machine learning model trained in advance on learning data, wherein the data is a plurality of sets of lesion information and at least one of sentence included in a medical diagnostic report based on the lesion information; acquiring output data from the learned model through inputting the data including lesion information of a subject to be diagnosed, wherein the output data includes at least one sentence of a medical diagnostic report based on the lesion information of the subject; displaying the at least one sentence included in the output data on a display; receiving correction data for the acquired output data via an input device to generate a corrected output data including an added portion that is added to the at least one sentence included in the output data and a deleted portion that is deleted from the at least one sentence included in the output data; storing, as relearning data of the learned model, a set of the input data including the lesion information of the subject and the corrected output data in a storage in a case where a value indicating a correction amount of the added portion and the deleted portion included in the corrected output data is equal to or greater than a threshold value, and retraining the learned model by using the stored relearning data in the storage.
 9. A non-transitory computer readable medium storing a program causing a computer to execute a process comprising: inputting data into a learned model, wherein the learned model is a machine learning model trained in advance on learning data, wherein the data is a plurality of sets of lesion information and at least one sentence included in a medical diagnostic report based on the lesion information; acquiring output data from the learned model through inputting the data including lesion information of a subject to be diagnosed, wherein the output data includes at least one sentence of a medical diagnostic report based on the lesion information of the subject; displaying the at least one sentence included in the output data on a display; receiving correction data for the acquired output data via an input device to generate a corrected output data including an added portion that is added to the at least one sentence included in the output data and a deleted portion that is deleted from the at least one sentence included in the output data; storing, as relearning data of the learned model, a set of the input data including the lesion information of the subject and the corrected output data in a storage in a case where a value indicating a correction amount of the added portion and the deleted portion included in the corrected output data is equal to or greater than a threshold value; and retraining the learned model by using the stored relearning data in the storage. 